The prospecting for underground oil and gas reservoirs is often performed by the use of seismic vibrations and waves which are intentionally input into the earth at a source location, and which are detected at remote locations by geophones (in the case of prospecting on land) or hydrophones (for offshore prospecting). The travel times of vibrations from the source to the detection locations is indicative of the depth of various geological features such as interfaces between sub-surface strata, and the presence of hydrocarbon reservoirs thereat, from which the seismic waves reflect.
In the field application of such prospecting, however, the detected vibrations generally include not only the reflected vibrations generated by the seismic source (i.e., the "signal"), but also often include vibrations from other sources which are not relevant to the seismic exploration (i.e., the "noise"). The sources of noise vibrations, examples of which include wind noise, electrical noise such as from nearby power lines, and cultural noises such as from automobile and cattle traffic, are quite numerous and varied. Accordingly, the cumulative noise generated by these various noise sources will include both organized (e.g., periodic) and random components, not only as a function of time (and frequency), but also which are spatially dependent, according to the type and position of the sources and the detectors.
The presence of noise vibrations detected with the signal vibrations makes seismic analysis more difficult, and accordingly less accurate. The problem of noise vibrations becomes more acute with the extremely low power of signal vibrations as they are reflected from very deep geological formations. Due to the wide variation in noise sources, both organized (e.g., periodic) and random (e.g., non-periodic or impulse) noise components, mere filtering of the detected vibrations will generally not result in an adequate seismic survey.
A conventional method for dealing with noisy seismic traces in a seismic shot record or seismic survey is the visual inspection of each shot record, followed by deletion of each seismic trace (i.e., time series of detected vibrations from one surface location for one seismic input) from the record prior to its analysis. This method is currently performed by a human operator at a computer workstation, where the shot record is displayed graphically and the operator identifies noisy traces using a mouse and cursor system. Since conventional shot records often include on the order of 100 traces each, and since conventional seismic surveys often include tens or hundreds of shot records, this manual editing is heavily labor intensive and is quite slow. Furthermore, such editing of noisy seismic traces is performed according to the subjective criteria of the individual operator, so that both the discarding of traces with valid data and the retention of noisy traces with limited useful data can often result. The seismic analysis performed on such an erroneously edited shot record is thus less accurate, as well as being quite expensive due to the large amount of skilled labor involved.
It is therefore quite apparent that automation of the seismic trace editing process is desirable. Various techniques have been proposed for the automation of editing large numbers of seismic traces. Examples of these techniques are described in Neff et al., "Noise suppression by the radial amplitude-slope rejection method", Geophysics, Vol. 51, No. 3 (March, 1986), pp.844-850; and Anderson et al., "Automatic Editing of Noisy Seismic Data", Geophysical Prospecting 36 (1989), pp. 875-892. These prior techniques have been based on statistical and mathematical attributes of the seismic signals, for example analyzing relative amplitude decay rates, or analyzing a combination of the amplitude and slope of such signals. However, due to variations in the nature of the noise sources and noise vibrations along the seismic profile and to other factors, it is questionable whether the reliability of these techniques is sufficient to allow significant reductions in cost and error over the purely manual process described hereinabove.
It is therefore an object of this invention to provide a method for automated seismic trace editing which uses an adaptive computer network such as a neural network.
Neural networks refer to a class of computations that can be implemented in computing hardware, or more frequently computer programs implemented on conventional computing hardware, organized according to what is currently believed to be the architecture of biological neurological systems. The distinguishing feature of neural networks is that they are arranged into a network of elements, mimicking neurodes of a human brain. In such networks, each element performs a relatively simple calculation, such as a weighted sum of its inputs applied to a non-linear function, such as a sigmoid, to determine the state of the output. Increased power of computation comes from having a large number of such elements interconnected to one another, resulting in a network having both parallel and sequential arrangements of computational elements. Proper setting of the weighting factors for each of the elements allows the network to perform complex functions such as image recognition, solving optimization problems, and the like.
The programming of conventional computer systems to operate as an artificial neural network is well known in the art, as described in Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, (Addison-Wesley Publishing Company, New York, 1989), incorporated herein by this reference. As described therein, such programming can be done in high level languages such as C.
A particular type of neural network which is of interest is referred to as the backpropagation network. Such a network generally includes multiple layers of elements as described above. Adaptation of the network to a particular task is done by way of "training" the network with a number of examples, setting the weighting factors for each element to the proper value. This training is accomplished by presenting inputs to the network, analyzing the output of the network, and adjusting the weighting factors according to the difference between the actual output and the desired output for the training example. Upon sufficient training, the network is adapted to respond to new inputs (for which the answer is not known a priori), by generating an output which is similar to the result which a human expert would present for the same inputs. An example of a conventional backpropagation algorithm for training a neural network of this type is described in Rumelhart et al., Parallel Distributed Processing (The MIT Press, Cambridge, Mass., 1988), incorporated herein by this reference.
It is therefore a further object of this invention to provide a neural network which is adapted to perform classification of valid and noisy seismic traces in a seismic survey, so that the noisy traces can be deleted from further analysis.
It is a further object of this invention to provide a neural network which performs such classification of seismic traces using both graphical data from the amplitude spectrum in the frequency domain and also information concerning particular attributes of the trace.
Other objects and advantages of the invention will be apparent to those of ordinary skill in the art having reference to the following specification in combination with the drawings.